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§ USE CASESEngagements · Made concrete

What an engagement actually looks like.

The shape of the work varies a lot. A one-person AI cowork setup wired into Claude or Microsoft 365. A team capability built inside a Microsoft 365 tenant. A finance workflow rebuilt around AI. A bespoke product rolled out across a year. Below are a few illustrative engagements grouped by who they lift, each mapped back to the services they draw on and, where useful, how the work can be sequenced. They’re a sample of what’s possible, not the full catalogue. If your situation isn’t here, it’s probably still a fit.

§ INDIVIDUAL AI BUILDOne person’s working setup, wired into the apps and data they live in.

Individual AI build

UC-01For knowledge workers who want AI woven into their day-to-day

Individual AI Cowork Build

A consultant, lawyer, or analyst loses hours each week to drafting, summarising, and digging through emails or documents. Off-the-shelf AI tools help, then they sit beside the real workflow rather than inside it. The work is personal. The tools should be too.

We sit with you for a few sessions, watch how a typical week actually runs, and identify the moments where AI would shave the most friction. From there we build a tailored Claude or ChatGPT setup wired into the apps you actually live in: Gmail or Outlook, Drive or OneDrive, Notion or SharePoint, your calendar, and whatever else carries the work. If you sit in a Microsoft 365 environment, the build can extend into the tenant via Graph permissions and a small custom MCP, so the assistant reaches your real data inside the boundary your IT team already trusts. You leave with a working setup, custom prompts and projects ready to use, and the confidence to extend it yourself.

§ TEAM CAPABILITY BUILDA focused capability that lifts a single team or function.

Team capability build

UC-02For teams whose answers live across SharePoint, Notion, and old email threads

Internal Knowledge Chatbot

Every business sits on a pile of internal knowledge. Policies, playbooks, decisions, the way a long-running customer was handled three years ago. None of it is searchable in any meaningful way. The same questions get asked, answered from memory, and lost again.

We start by mapping where your knowledge actually lives, then prepare and embed it in a retrieval system tuned to your domain. The result is a chatbot embedded in Slack, Teams, or wherever your team already works. Every answer carries a citation back to the source document, so anything the system says can be checked. The system grows with your knowledge base over time.

UC-03For sales, support, and service teams running heavy phone volume

Voice Agent and Call Summarisation

Inbound calls land on a team that handles them well, then loses most of the value the moment the call ends. Notes get summarised in a hurry, follow-ups slip, patterns across calls go unnoticed. Outbound work has the opposite problem: every dial costs human time the team does not have.

We deploy voice agents tuned to your brand and your scripts, integrated with your telephony. Calls that fit a known pattern resolve end-to-end. Calls that need a person route cleanly with full context. Every call gets summarised and structured into your CRM. Quality monitoring runs from day one so the experience holds up under real volume.

UC-04For teams whose AI tools cannot reach the data and actions that matter

Custom MCP Server for Your CRM

You have AI in place. An agent or two, maybe a chatbot or a Claude setup. The team uses them, then the moment a task needs real data from your CRM, your billing system, or your project tool, the AI hits a wall. The work falls back to a tab and a copy-paste.

We build a custom MCP (Model Context Protocol) server that exposes the right slices of your stack to AI safely. Your agents can read records, run searches, and trigger actions inside the same systems your team uses. Permissions and audit trails are designed in from the first build. Once it exists, every future AI capability you add can plug into the same infrastructure.

UC-05For Microsoft-shop teams that want AI capability lifted across the team without leaving the tenant

AI Capability Inside Microsoft 365

The whole team lives in Outlook, Teams, SharePoint, and OneDrive. Copilot licences are issued and barely used past the surface. IT has hard rules about where data can travel, which rules out most off-the-shelf AI tooling. The team needs capability built inside the tenant boundary, not bolted on around it.

We sit with the team to find the workflows that pay off first, then build inside the tenant. Custom agents in Copilot Studio where they fit the work, SharePoint-grounded retrieval where the answers live in documents, Teams-embedded agents for in-flow tasks, and Graph-scoped automations across Outlook and OneDrive. Permissions, data classification, and audit posture are designed in from the first build, so security signs off without rework. The team ends with capability they own, governed by the tenant they already trust.

UC-06For finance teams drowning in invoices, statements, and reconciliations

AR and AP Automation with AI

Accounts payable and receivable run on PDF statements, email threads, and someone keying figures into a spreadsheet. Cycle times stretch. Errors creep in. Month-end becomes an event the team dreads. Most of the work is pattern matching that AI is now genuinely good at.

We start by tidying the data foundation: vendor masters, customer masters, GL coding rules, and the integrations into your accounting platform. Then we build the workflow. Invoices ingested and parsed, exceptions routed for review, payments and dunning sequenced. AI handles the judgement calls a junior accountant would, with a human in the loop on anything ambiguous. The system gets better as it sees more of your real volume.

UC-07For support teams whose ticket queue keeps outpacing the team

Tier-1 Customer Support Agent

Most of your support volume is repeat questions answered the same way every week. Tier-1 spends its day on those, leaving senior staff to firefight the genuinely hard tickets later than they should. Knowledge is in the macros, the wiki, and people’s heads, in roughly equal measure.

We build a support agent grounded in your help centre and ticket history, deployed in your existing channel. Web chat, email, in-product, or all three. The agent handles known patterns end-to-end and hands off to humans with full context when it cannot. We stand up an evaluation framework alongside it, so quality can be measured against real conversations and tracked release over release.

UC-08For sales leaders whose reps lose hours to admin instead of selling

Sales Operations Agent

Reps spend a fraction of their week actually selling. The rest goes to logging calls, updating pipeline, chasing context across tools, and assembling the briefs they need before the next conversation. The CRM is a system of record only because someone forces it to be.

We build an agent that lives inside your CRM via custom tooling. It drafts call notes and follow-ups, surfaces the right context before every meeting, and triggers the workflow steps a manager would normally chase. Pipeline updates happen as a by-product of the work itself. The first phase ships in weeks; later phases extend into forecasting and account planning.

UC-09For teams running their operations on a stack of spreadsheets and SaaS workarounds

Internal Tool with an Embedded Agent

A core part of your operation runs on a spreadsheet that was always meant to be temporary. The team has bent the workflow around it for years. The right answer is a small custom tool. The wrong answer was always to keep waiting because building it was too expensive.

We design and build the tool itself. A clean interface, the right data model, integrations into the systems it touches. Inside it lives an agent scoped to the work the team actually does on this surface. Drafting, summarising, classifying, or triggering downstream actions. AI-assisted development keeps the build affordable at small engagement sizes. A first usable release goes live in weeks.

UC-10For regulated teams tracking obligations across long, dense documents

Compliance and Document Intelligence

Compliance, legal, and risk teams work in a world of long-form documents that change often and matter when they do. Manual review keeps up by working harder. Misses are rare and expensive when they happen. The work needs structure, traceability, and evaluation, all together.

We organise the document corpus, build retrieval and extraction tuned for your obligation types, and stand up an evaluation framework so quality is measurable from day one. Reviewers get an interface that surfaces the right clauses, flags changes, and routes anything ambiguous to a human. Every output traces back to a specific source paragraph.

§ COMPANY TRANSFORMATIONA wider operating-model change shaped around budget, dependencies, and adoption.

Company transformation

UC-11For teams whose existing software cannot keep up with how they actually run

Full Product, Phased Rollout

The off-the-shelf stack has reached its limits. The workflow has been bent around it for years. A bespoke product would change the trajectory of the business, and AI-assisted development now makes that build feasible at a sensible price. The risk is doing it the old way, in one big bang, and missing.

We split the build into phases that each deliver something usable on their own. Phase one is a small slice of the product that solves a single painful workflow. Phase two layers in agents and automation. Later phases extend the product across the business. Every phase ships, gets used, and earns the next.

§ PHASESHow this transformation runs
PH-01

Understand

Operations audit, data audit, and a map of where the current stack is hurting most. Output is the phased plan and the slice of product to build first.

PH-02

Architect

Product, data, and integration architecture. The decisions made here are the ones every later phase will live inside, so they get the time they deserve.

PH-03

Build

First usable release in weeks. Each subsequent phase is scoped to ship working software, not promise it. AI capability lands inside the product as the product matures.

PH-04

Evolve

We stay with the product after launch. Refinement, measurement, and the next phase get planned alongside a team that already knows what you have.

UC-12For companies rolling AI capability out across the whole team

AI-Fluent Workforce Programme

AI is going to reshape how every role works. The companies that wait for a perfect plan will fall behind the ones whose people are already using AI well. A workforce-wide rollout has to move at a pace each team can absorb, or it stalls in change-management theatre.

We start with a small group of early adopters whose work is most reshaped by AI. They get hands-on enablement and the role-specific tools their work calls for, landing inside whichever ecosystem the company already runs (Microsoft 365, Google Workspace, Claude, ChatGPT, or a mix). Their results become the proof point for the next group. Strategy work in the background sequences which roles come next, what infrastructure they will share, and how the programme connects to wider transformation work.

§ PHASESHow this transformation runs
PH-01

Understand

Workforce mapping and an honest read on where AI fits each role. The output names the first cohort and the tools they will get.

PH-02

Architect

Strategy and infrastructure design. Role-specific tooling is sequenced, shared infrastructure decisions are made once, and the rollout map is set.

PH-03

Build

Cohort by cohort. Each group gets enablement plus the tools their work calls for. Results from each cohort feed the next.

PH-04

Evolve

Iteration, role expansion, and feeding what the workforce now does well into wider transformation work.

UC-13For businesses ready to rebuild operations around AI rather than bolt it on

End-to-End Operations Overhaul

The business has outgrown the way it runs. Disconnected tools, manual handoffs, and tribal knowledge hold the operation together at the cost of speed and headcount. A tactical fix here or there will not change the curve. The operating model itself needs to change.

We map the full transformation before committing to any single phase. Almost always, the first phase is data foundation work. From there, the highest-pain workflow gets automated, then an agent layer lands on top, then a custom product emerges where the off-the-shelf stack runs out. An evaluation layer comes in alongside the AI systems so quality stays measurable as the operation evolves.

§ PHASESHow this transformation runs
PH-01

Understand

Operations and data audit. Output is a phased roadmap, costed and sequenced, with the first phase scoped tightly enough to start.

PH-02

Architect

Data architecture, automation orchestration, and the integration layer agents and products will live on. Decisions here determine what every later phase can do.

PH-03

Build

Workflow by workflow. Each release is small enough for the team to absorb before the next lands. AI capability lands progressively, never on a cutover date.

PH-04

Evolve

Evaluation, refinement, and the planning of whatever the business needs next, alongside a team that already knows what you have.

Next step

Let's scope the right build.

A 30-minute conversation. No obligation. We'll talk through the outcome that matters, the wider context, and whether the work calls for one focused build or several phases.

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